code: def test_openvino_vpu_quatize(self):
      # test whether contains VPU
      from openvino.runtime import Core
  
      core = Core()
      devices = core.available_devices
      vpu_avaliable = any("VPUX" in x for x in devices)
  
      if vpu_avaliable is False:
          return
  
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((1, 3, 256, 256))
      x2 = torch.rand((10, 3, 256, 256))
  
      # test VPU int8
      openvino_model = InferenceOptimizer.quantize(
          model,
          calib_data=x,
          input_sample=x,
          accelerator="openvino",
          device="VPUX",
          precision="int8",
      )
      result = openvino_model(x)
      # VPU don't support dynamic shape
      with pytest.raises(RuntimeError):
          openvino_model(x2)
  
      # test VPU fp16
      model = resnet50()
      with pytest.raises(RuntimeError):
          openvino_model = InferenceOptimizer.quantize(
              model,
              input_sample=x,
              accelerator="openvino",
              device="VPUX",
              precision="fp16",
          )
  
      openvino_model = InferenceOptimizer.quantize(
          model,
          input_sample=x,
          accelerator="openvino",
          device="VPUX",
          precision="fp16",
          mean_value=[123.68, 116.78, 103.94],
      )  # first type of mean value
      result = openvino_model(x)
  
      openvino_model = InferenceOptimizer.quantize(
          model,
          input_sample=x,
          accelerator="openvino",
          device="VPUX",
          precision="fp16",
          mean_value="x[123.68,116.78,103.94]",
      )  # the second type of mean value
      result = openvino_model(x)
  
      # VPU don't support dynamic shape
      with pytest.raises(RuntimeError):
          openvino_model(x2)
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 10 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_openvino_vpu_quatize() without return types